Development of a program theory for shared decision-making: a realist review protocol
Bibliographic record
Abstract
BACKGROUND: The practicality of applying evidence to healthcare systems with the aim of implementing change is an ongoing challenge for practitioners, policy makers, and academics. Shared decision- making (SDM), a method of medical decision-making that allows a balanced relationship between patients, physicians, and other key players in the medical decision process, is purported to improve patient and system outcomes. Despite the oft-mentioned benefits, there are gaps in the current literature between theory and implementation that would benefit from a realist approach given the value of this methodology to analyze complex interventions. In this protocol, we outline a study that will explore: "In which situations, how, why, and for whom does SDM between patients and health care providers contribute to improved decision making?" METHODS: A seven step iterative process will be described including preliminary theory development, establishment of a search strategy, selection and appraisal of literature, data extraction, analysis and synthesis of extracted results from literature, and formation of a revised program theory with the input of patients, physicians, nurse navigators, and policy makers from a stakeholder session. DISCUSSION: The goal of the realist review will be to identify and refine a program theory for SDM through the identification of mechanisms which shape the characteristics of when, how, and why SDM will, and will not, work. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017062609.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.260 | 0.285 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.066 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".